Aligning a KM Strategy and Developing KM Capabilities
Bibliographic record
Abstract
Knowledge capitalization has become a major economic driver in business today and has created enormous requirements for organizations as they reconsider their goals and adapt their business strategies. However, the definition of knowledge management in an organizational context is a difficult task to realize (Spiegler, 2000). Although knowledge is a critical resource, it is generally poorly managed (Earl, 2001). Good knowledge management in an organization is likely to help achieve business goals, but require an alignment of knowledge management strategies (KMS) and business strategies (BS). Such alignment can be an effective approach to enhancing interactions and to applying knowledge. This chapter provides, first, a KMS and BS alignment framework and taxonomy in which concepts, links (contextual links among concepts), actors, actions and processes are defined and described to show how they provide the potential for effective knowledge management through alignment and interaction in an organization. Secondly, a KM capabilities framework and taxonomy in which three main dimensions and specific features is presented. The framework presented here is for managers in companies and organizations to use to align their KM strategies with business strategies to improve performance involving financial growth, cost reduction, and customer satisfaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".